Find research datasets worth reusing

opencc-by-4.0Dec 2021View details →

Data repository for Lin et al. (2022) "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling"

This dataset contains the necessary data and plotting tools supporting the paper titled "Thermospheric neutral density variation during the "SpaceX" storm: Implications from physics-based whole geospace modeling", by Lin et al., 2022. The data set contains thermospheric mass density simulated by MAGE, TIEGCM, DTM, and MSIS for the 1-6 February 2022 geomagnetic storm event.

opencc-by-4.0Dec 2019View details →

Model and observational data for Morrison et al. (2020) JAMES paper entitled "Confronting the challenge of modeling cloud and precipitation microphysics"

This dataset was used to generate plots for a paper conditionally accepted in the Journal of Advances in Modeling Earth Systems (JAMES), an AGU journal. It comprises four different data sources: WRF simulations, radar observations, an idealized steady-state column model called the Bayesian Observationally-Constrained Statistical Scheme (BOSS), and idealized column rainshaft model using bin microphysics. The WRF simulations consist of output from a set of 11 runs using either bulk or bin microphysics schemes. The version of WRF is V3.9.1. To limit storage requirements, we have only saved a single time-slice of output at hour 6 of the simulations, along with the namelist.input file to generate these runs. The observational data are gridded and rotated composite NEXRAD reflectivity measurements from central Oklahoma. BOSS model output consists of microphysical parameter probability density functions and profiles of (as described in the meta-file for these data). The idealized bin microphysics rainshaft model output consists of profiles of drop mean size, reflectivity factor, precipitation rate, and drop concentration at two different time slices (described in the meta-data file for these data). We are requesting these data be added to the managed NCAR repository so they may be accessible to the community per AGU publication policy and in accordance with a U.S. DOE grant that partially supported this work.

opencc-by-4.0Dec 2020View details →

Data for GRL Article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model"

This repository contains mandatory material to reproduce the results of the GRL article "Resolved Convection Improves the Representation of Equatorial Waves and Tropical Rainfall Variability in a Global Nonhydrostatic Model" [Paper #2021GL093265RR].

opencc-by-4.0Aug 2024View details →

Supplementary Data for "Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes"

This package contains supplementary data not directly included in the paper, including per-commit results for each prototype and the prompts used in the proof-of-concept implementations.

opencc-by-4.0May 2023View details →

Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"

Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans.  URL of the manuscript:  https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101 DOI of the manuscript:  https://doi.org/10.1029/2023JC020101

openother-openNov 2023View details →

Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling

egon-data provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project eGon. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon <a href="https://ego-n.org/">project website</a> or its <a href="https://github.com/openego/eGon-data">Github repository.</a> egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data. The following data sets are part of the available data bundle: <ol> <li> climate_zones_germany <ul> <li> Climate zones in Germany </li> <li> source: Own representation based on DWD TRY climate zones </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> cutouts <ul> <li> Weather data from Europe in 2011. Source: ERA5 </li> </ul> </li> <li> demand_regio_backup <ul> <li> Electricity and heat demands </li> </ul> </li> <li> emobility <ul> <li> Data on eMobility mit_trip_data: motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR). </li> <li> Reiner Lemoine Institut, June 2022 </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> entsoe <ul> <li>   </li> </ul> </li> <li> gas_data <ul> <li> CH4 infrastructure </li> <li> Biogas demand </li> <li> CH4 demand </li> <li> Source: SciGRID_gas </li> </ul> </li> <li> geothermal_potential <ul> <li> Spatial distribution of deep geothermal potentials in Germany </li> <li> source: <a href="https://doi.org/10.3390/en11020332">Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook</a> </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> household_electricity_demand_profiles <ul> <li> Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children. The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012. The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh. A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> household_heat_demand_profiles <ul> <li> Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016 </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> hydrogen_network <ul> <li> Planned H2 infrastructure </li> <li> Forecast H2 demand </li> <li> Source: fnb-gas </li> </ul> </li> <li> hydrogen_storage_potential_saltstructures <ul> <li> The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5.. </li> <li> Source: Flach lagernde Salze, (c) BGR Hannover, 2021. Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. & Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., & Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen, Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) – Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht. Hannover: BGR. </li> <li> License: The original data are licensed under the GeoNutzV, see <a href="https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf">https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf</a> </li> </ul> </li> <li> industrial_gas_demand </li> <li> industrial_sites <ul> <li> Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site. </li> <li> source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767 </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> mastr_geocoding </li> <li> nep2035_version2021 <ul> <li> Data extracted from the German grid development plan - power </li> <li> source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0 </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> pipeline_classification_gas <ul> <li> Parameters for the classification of gas pipelines </li> <li> source: Single parameters extracted from <a href="https://www.econstor.eu/bitstream/10419/173388/1/1011162628.pdf">Electricity, Heat and Gas Sector Data for Modelling the German System</a> </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> pypsa_eur </li> <li> regions_dynamic_line_rating <ul> <li> German regions suitable to model dynamic line rating </li> <li> source: Own representation based on <a href="https://www.transnetbw.de/files/pdf/netzentwicklung/netzplanungsgrundsaetze/UENB_PlGrS_Juli2020.pdf">Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)</a> </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> re_potential_areas <ul> <li> Eligible areas for wind turbines and ground-mounted PV systems. </li> <li> Reiner Lemoine Institut, January 2022 </li> <li> License: Attribution 4.0 International (CC BY 4.0) </li> </ul> </li> <li> wind_offshore_status2019 <ul> <li>   </li> </ul> </li> <li> WZ_definition <ul> <li> Definitions of industrial and commercial branches </li> <li> source: <a href="https://www.destatis.de/static/DE/dokumente/klassifikation-wz-2008-3100100089004.pdf">Klassifikation der Wirtschaftszweige (WZ 2008)</a> </li> <li> Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet. </li> </ul> </li> <li> zensus_households <ul> <li> Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution. </li> <li> source: Data retrieved from <a href="https://ergebnisse2011.zensus2022.de/datenbank/online">Zensus Datenbank</a> by performing these steps: <ul> <li> Search for: "1000A-2029" </li> <li> or choose topic: "Bevölkerung kompakt" </li> <li> Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)" </li> <li> Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration. </li> </ul> </li> <li> Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet. </li> </ul> </li> <li> zensus_population </li> <li> district_heating_shares_egon.csv </li> </ol>

opencc-by-4.0Sep 2023View details →

Data for "Millennial modulation of Atlantic Multidecadal Variability in a climate model"

Processed data and Jupyter notebooks for manuscript "Millennial modulation of Atlantic Multidecadal Variability in a climate model" by Joakim Kjellsson and Wonsun Park

Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks

Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively. Filenames denote the structure of the SS profile:  ???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}   LT = elastic lithosphere thickness (km) UMV = upper mantle viscosity (1E21 Pa s) LMV = lower mantle viscosity (1E21 Pa s) i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile 96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity   The columns of the input files are as follows: LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0) units for the above are as follows: km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m,  mm/year where 'days since 2000' assumes exactly 365.25 days per year.

Electron concentration profiles calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data

The files contain electron concentration Ne profiles during solar X-ray flares that occurred on 9-11 June 2014. The altitude range is 50-90 km.Values of electron concentration were calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data (MSIS neutral atmosphere model and Aura satellite measurements). Results are obtained for ten VLF paths: from European transmitters ICV, TBB, GQD, GBZ, and DHO to Mikhnevo geophysical observatory (55°N 38°E) and A118 SID station (43°N 1°E).The data is presented as MATLAB files. Each .mat file contains data and variable "description" with data's structure information.

Data for "Deciphering the code of viral-host adaptation through maximum entropy models"

Data needed to reproduce the figures of the paper "Deciphering the code of viral-host adaptation through maximum entropy models", following the instructions provided in <a href="https://github.com/adigioacchino/MENB_snakemake">this GitHub repository</a>.

opencc-by-4.0Jul 2023View details →

Data related to "Modeling the Diverse Effects of Divisive Normalization on Noise Correlations"

Data files for the paper, Modeling the Diverse Effects of Divisive Normalization on Noise Correlations, available on <a href="https://www.biorxiv.org/content/10.1101/2022.06.08.495145v3.abstract">bioRxiv</a> For the corresponding code toolbox, please see the <a href="https://github.com/oren-weiss/pairwiseRatioGaussians">Github repository</a> Please cite as:<blockquote>Modeling the Diverse Effects of Divisive Normalization on Noise CorrelationsOren Weiss, Hayley A. Bounds, Hillel Adesnik, Ruben Coen-CaglibioRxiv 2022.06.08.495145; doi: https://doi.org/10.1101/2022.06.08.495145</blockquote>

Model codes, data, and plot scripts for the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".

The model codes, data, and plot scripts used in the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".<ul><li>Figs&NCL: the NCL scripts and figures used in the paper.</li><li>Mods: modified CAM model code.</li><li>PostFortran: the Fortran code for analysing the model results (post-processing) that produces the final results used for making plots.</li><li>Results4Plots: the final results used for making plots.</li></ul>

Gastroplus software data and model

Gastroplus software data and model screen print for the software data for both nasal and oral routes. The taken photo including all the data and information and graphs.

"An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model" train and test data

<ul><li>Model for the article "An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model".</li><li>The  .pth file is the pre-trained PtyNet-S model and the fine-tuned PtyNet-B model.</li><li>Please contact panxy@ihep.ac.cn if you have any questions.</li></ul>

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (2/4)

This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.All zip files should unzipped into a parent folder called /data inside the Github repository above.Important: Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload 2 out of 4. The other 3 uploads can be found at:  Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)For more information please contact: salvador.dura-bernal@downstate.edu

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (3/4)

This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.All zip files should unzipped into a parent folder called /data inside the Github repository above.Important: Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload 3 out of 4. The other 3 uploads can be found at:  Upload 1/4: <a href="http://doi.org/10.5281/zenodo.10066993">http://doi.org/10.5281/zenodo.10066993</a> (https://zenodo.org/uploads/10066993)Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)For more information please contact: salvador.dura-bernal@downstate.edu

opencc-by-4.0Dec 2023View details →

Models and data for "Amortized reparametrization: Efficient and Scalable Variational Inference for Latent SDEs"

Open the record for dataset details and reuse information.

Data associated with Cell Reports publication: Dura-Bernal, Griffith, et al. 2023, "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics" (1/4)

This dataset includes experimental data used to constrain and validate the model, and model simulation output data for the following Cell Reports publication: <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6">https://www.cell.com/cell-reports/fulltext/S2211-1247(23)01390-6</a>The source code for the associated A1 model and data analysis can be found here: <a href="https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data">https://github.com/NathanKlineInstitute/Macaque_auditory_thalamocortical_model_data</a>.All zip files should unzipped into a parent folder called /data inside the Github repository above.Important: Due to the Zenodo size limit, this dataset is split among 4 Zenodo uploads. This is upload 1 out of 4. The other 3 uploads can be found at:  Upload 2/4: <a href="http://doi.org/10.5281/zenodo.10069553">http://doi.org/10.5281/zenodo.10069553</a> (https://zenodo.org/uploads/10069553)Upload 3/4: <a href="http://doi.org/10.5281/zenodo.10071726">http://doi.org/10.5281/zenodo.10071726</a> (https://zenodo.org/uploads/10071726)Upload 4/4: <a href="http://doi.org/10.5281/zenodo.10072277">http://doi.org/10.5281/zenodo.10072277</a> (https://zenodo.org/uploads/10072277)For more information please contact: salvador.dura-bernal@downstate.edu

Data for figures in: "The DESI One-Percent Survey: A concise model for galactic conformity of ELGs"

Supplementary material to DESI's publication "The DESI One-Percent Survey: A concise model for galactic conformity of ELGs" to comply with the data management plan. Details of each file can be found in "ReadMe.txt".

MME-only models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"

This tar file contains all 100 trained models in the MME-only ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data).  To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.